self-llm is an open source educational project created by the Datawhale community that serves as a practical guide for deploying, fine-tuning, and using open-source large language models on Linux systems. The repository focuses on helping beginners and developers understand how to run and customize modern LLMs locally rather than relying solely on hosted APIs. It provides step-by-step tutorials covering environment setup, model deployment, inference workflows, and efficient fine-tuning techniques such as LoRA and parameter-efficient training. The project also includes guides for integrating models into real applications, including command-line interfaces, web demos, and frameworks like LangChain. By combining theory, configuration instructions, and runnable examples, self-llm lowers the barrier to entry for students and engineers who want to experiment with open-source models.
Features
- Step-by-step Linux environment setup for running LLMs
- Deployment tutorials for major open-source models
- Guides for full and parameter-efficient fine-tuning methods
- Examples integrating LLMs with frameworks like LangChain
- Instructions for command-line and web demo deployment
- Educational notebooks and practical code examples